Hierarchical method for determining the number of clusters

Ruan Jian Xue Bao/Journal of Software

Volumn 19, Issue 1, 2008, Pages 62-72

  Chen, Li Fei ^a   Jiang, Qing Shan ^a   Wang, Sheng Rui ^b  

^a XIAMEN UNIVERSITY   (China)

^b UNIVERSITÉ DE SHERBROOKE   (Canada)

Author keywords

Clustering; Clustering validity index; Hierarchically clustering; Number of cluster; Statistics

Indexed keywords

CLUSTERING ALGORITHMS; DATA MINING; STATISTICS;

CLUSTER NUMBER; CLUSTERING FEATURE (CF); CLUSTERING QUALITY; CLUSTERING VALIDITY INDEX; DATASET; GEOMETRIC FEATURES; HIERARCHICALLY CLUSTERING; NOISY DATA; TRAIL ERROR METHOD;

CLUSTER ANALYSIS;

EID: 39749159225     PISSN: 10009825     EISSN: None     Source Type: Journal    
DOI: 10.3724/SP.J.1001.2008.00062     Document Type: Article

References (21)

1. Berkhin P, Survey of clustering data mining techniques. Technical Report, San Jose: Accrue Software, 2002.
2. Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a dataset via the gap statistic. Technical Report, 208, Stanford University, 2000.
3. Still S, Bialek W. How many clusters? An information-theoretic perspective. Neural Computation, 2004, 16(12): 2483-2506.
4. Kapp AV, Tibshirani R. Are clusters found in one dataset present in another dataset? Biostatistics, 2007, 8(1): 9-31.
5. Dudoit S, Fridlyand J. A prediction-based resampling method for estimating the number of clusters ia a dataset. Genome Biology, 2002, 3(7): 1-21.
6. Halkidi M, Batistakis Y, Vazirgiannis M. Clustering validity checking methods: Part II. ACM SIGMOD Record Archive, 2002, 31(3): 19-27.
7. Bouguessa M, Wang S, Sun H. An objective approach to cluster validation. Pattern Recognition Letters, 2006, 27(13): 1419-1430.
8. Xie X, Beni G. A validity measure for fuzzy clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1991, 13(8): 841-847.
9. Sun H, Wang S, Jiang Q. FCM-Based model selection algorithms for determining the number of cluster. Pattern Recognition, 2004, 37(10): 2027-2037.
10. Fan J, Wu C. Clustering validity function based on possibilistic partition coefficient combined with fuzzy variation. Journal of Electronics and Information Technology, 2002, 24(8): 1017-1021 (in Chinese with English abstract).
11. Sun C, Wang J, Pan J. Research on the method of determining the optimal class number of fuzzy cluster. Fuzzy Systems and Mathmatics, 2001, 15(1): 89-92 (in Chinese with English abstract).
12. Hong Z, Jiang Q, Dong H, Wang S. A new cluster validity index for fuzzy clustering. Computer Science, 2004, 31(10): 121-125 (in Chinese with English abstract).
13. Zhu K, Su S, Li J. Optimal number of clusters and the best partition in fuzzy C-mean. Systems Engineering-Theory and Practice, 2005, 25(3): 52-61 (in Chinese with English abstract).
14. Yu J, Cheng G. Search range of the optimal number of clusters in fuzzy clustering. Science in China (Series E), 2002, 32(2): 274-280 (in Chinese with English abstract).
15. Woo KG, Lee JH, Kim MH, Lee YJ. FINDIT: A fast and intelligent subspace clustering algorithm using dimension voting. Information and Software Technology, 2004, 46(4): 255-271.
16. Ester M, Kriegel HP, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis E, Han JW, Fayyad UM, eds. Proc. of the ACM-SIGKDD. Portland: AAAI Press, 1996. 226-231.
17. Zhang T, Ramakrishnan R, Livny M. BIRCH: An efficient data clustering method for very large databases. In: Jagadish HV, Mumick IS, eds. Proc. of the ACM-SIGMOD. New York: ACM Press, 1996. 103-114.
18. Brecheisen S, Kriegel HP, Pfeifle M. Multi-Step density-based clustering. Knowledge and Information Systems, 2006, 9(3): 284-308.
19. Foss A, Zaiane OR. A parameterless method for efficiently discovering clusters of arbitrary shape in large datasets. In: Kumar V, Tsumoto S, eds. Proc. of the TCDM. Los Alamitos: IEEE Computer Society Press, 2002. 179-186.
20. Kim M, Yoo H, Ramakrishna RS. Cluster validation for high dimensional datasets. In: Proc. of the AIMSA. LNCS 3192, Berlin, Heidelberg, 2004. 178-187.
21. Agrawal R, Gehrke J, Gunopulos D, Raghavan P, Automatic subspace clustering of high dimensional data. Data Mining and Knowledge Discovery, 2005, 11(1): 5-33.